![]() We needed help from external contractors to collect training data and continually assess the quality of our models. This was a highly complex, truly full-stack, and 3-year long project that occupied a full 10-person ML team. ![]() The ML team at GoodNotes is proud to have shipped our in-house handwriting recognition engine at the end of 2022, which now supports 12 languages and powers millions of GoodNotes users. Tell us more about how you and the team shipped the handwriting recognition feature? ML engineers often fail to take their minds off the models they are training, so we periodically pull up ClearML or TensorBoard to monitor the training progress even after getting home. After the meetings, you continue to sprint toward the same goal you’ve set for a couple more hours: investigating another model architecture, having an ad-hoc chat with a colleague about memory optimization, or opening a pull request for your work. (If you’re in Europe, this happens in the morning). If you’re in Hong Kong, you then usually reach the time for cross-functional meetings, reflecting on the progress and planning targets for the rest of the sprint. optimizing hyperparameters, setting up the right logging and configuration). This often involves training a ML model, which may take up to 2 days, so we try to make sure we do the best preparation work we can (eg. After lunch, you shift gears to the meat of your project, churning out code and iteratively refining it. Over a long lunch and coffee, you unwind a bit, but often end up organically discussing the project with your peers, or a newly released large language model. Time permitting, you get started with your main task of the day, be it whipping out a technical proposal or starting to investigate a new model. In the morning, you plan your tasks for the day, check in on Slack for company- or team-wide updates, do some code reviews (or improve your own pull request based on the reviews received), or read a new ML paper. That being said, here’s what an average day might look like in the company right now. On the other hand, an engineer who is currently focused on a single module within a project spends more time writing code and technical plans. I currently own two projects that span multiple teams, so 60% of my time is spent in meetings, writing plans across team members, reviewing proposals and code, or giving feedback. How an engineer at GoodNotes spends their time is unique to their project and their working style. What does an average day for a machine learning engineer look like at GoodNotes? And just for fun, did you know that one of the first tests that early machine learning algorithms needed to pass was to distinguish between pictures of chihuahuas and muffins? And today, we're teaching machines to create beautiful movies all by themselves - no humans needed! Just 7 years ago, a computer beat a world champion at Go, a game that was once thought impossible for a machine to master, by having a computer play itself repeatedly, simulating millions of real games. While that’s pretty neat, scientists have taken this idea further and have come a really long way in teaching machines to recognize patterns and make predictions based on data. After learning patterns to classify between them, the computer gains the ability to “guess” which of them is in new photos. We would give it millions of photos containing cars, dogs, doctors, apricots, and so on. We achieve this by feeding the computer with examples - lots and lots of examples - kind of like how humans learn stuff!Ĭonsider teaching a computer to label objects in photos. Machine learning is the process of teaching computers to perform tasks that would usually only be possible for humans. First things first, how would you explain machine learning to a five-year old? Hi Angus! Thanks for having a chat with us.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |